EP2907120A1 - Estimating a street type using sensor-based surroundings data - Google Patents
Estimating a street type using sensor-based surroundings dataInfo
- Publication number
- EP2907120A1 EP2907120A1 EP13771129.7A EP13771129A EP2907120A1 EP 2907120 A1 EP2907120 A1 EP 2907120A1 EP 13771129 A EP13771129 A EP 13771129A EP 2907120 A1 EP2907120 A1 EP 2907120A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- estimate
- road type
- decision tree
- state machine
- estimation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000003066 decision tree Methods 0.000 claims abstract description 56
- 238000000034 method Methods 0.000 claims abstract description 28
- 238000005259 measurement Methods 0.000 claims abstract description 7
- 238000012797 qualification Methods 0.000 claims description 14
- 230000006870 function Effects 0.000 claims description 9
- 238000010801 machine learning Methods 0.000 claims description 5
- 238000012549 training Methods 0.000 claims description 4
- 230000009849 deactivation Effects 0.000 claims description 2
- 238000012545 processing Methods 0.000 claims description 2
- 239000013598 vector Substances 0.000 description 11
- 230000007613 environmental effect Effects 0.000 description 6
- 238000011161 development Methods 0.000 description 5
- 230000018109 developmental process Effects 0.000 description 5
- 230000007704 transition Effects 0.000 description 4
- 230000001419 dependent effect Effects 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 230000006698 induction Effects 0.000 description 1
- 230000001939 inductive effect Effects 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 230000000644 propagated effect Effects 0.000 description 1
- 238000002604 ultrasonography Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/588—Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/254—Fusion techniques of classification results, e.g. of results related to same input data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
- G06V10/80—Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
- G06V10/809—Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level of classification results, e.g. where the classifiers operate on the same input data
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2552/00—Input parameters relating to infrastructure
- B60W2552/05—Type of road, e.g. motorways, local streets, paved or unpaved roads
Definitions
- the invention relates to a method for estimating a road type and to a device set up for this purpose.
- a navigation system to determine the type of road on which a vehicle is located.
- the position of the vehicle is determined by means of a satellite-based positioning system and the type of road is determined by means of a stored digital map in which the road and its type are also stored.
- Types of roads are, for example, inner city, highway and highway.
- the navigation system is needed to detect the road type. Frequently, such is not available even in modern vehicles for cost reasons.
- the present task to those skilled in the art is to provide an improved method for estimating a road type and to provide a corresponding device.
- a method for estimating a road type performed by an electronic computing device includes: receiving on one Sensor measurement based environment data; Estimating the road type using a decision tree based on the surrounding data; Estimating the road type using a state machine based on the environment data; Form an overall estimation of the road type based on the estimation using the decision tree and the estimation using the state machine.
- the accuracy of estimating the road type can be improved.
- a combination of an estimate by means of a decision tree and an estimate by means of a state machine has proven to be particularly advantageous.
- the method enables the combination of a machine learning trained decision tree and a state machine based on human experience in guessing the road type.
- the decision tree is induced by per se known methods of machine learning, based on recorded ride data (training data), which comprise sensor-based environment data and, as an estimation result of the road type, the road types determined by a navigation system.
- training data comprise sensor-based environment data and, as an estimation result of the road type, the road types determined by a navigation system.
- environmental data is associated with road types.
- the state machine comprises transition rules between states representing road types and possibly the state "no estimation of the road type possible", the transition rules being defined by human observation With the help of this structure of the estimation, particularly good estimation results are achieved Motorways recognized with an accuracy of over 90%.
- Environment data may, in particular, be direct measured values from sensors, processed measured values, such as, for example, detected objects in a camera recording or radar scan.
- environment data describe characteristics of the environment of the vehicle. Suitable sensors are all sensors of the vehicle, for example camera systems, 3D camera systems, lateral cameras, reversing cameras, ultrasound and lidar sensors, speedometers, yaw rate sensors or steering angle sensors.
- An example of environmental data is processed data from a camera shot indicating that the track width is 2.5 m, and that the lane is a lane.
- Environment data can be data from several sensors, which are present as bundles, for example in a vector.
- An environment data vector may include, for example, the track width detected in a camera shot, the number of signs detected by camera recording, oncoming traffic detected by a camera shot or radar scan, detected speed limits, a street lamp detected with a camera disposed on the side of the vehicle, obstacles detected by ultrasonic sensors, or the like Distance to the front vehicle, the detected with Lidarsensoren brake threshold and / or detected with capacitive sensors height of the chassis.
- the state machine may be based on human experience in estimating the road type. This experience may be recorded in all or some transitional rules between states.
- the states represent the road types (eg, city, highway, and highway) and the state that no estimation of the road type is possible, or equivalent, that no determination of the road type is possible.
- the transition rules are based on Boolean expressions that link input data, ie environment data.
- the total estimated estimate can be output for a certain distance of the vehicle. Furthermore, it can be provided, the output of Estimate depending on the particular type of road to make or keep. Thus, it may be provided that, if the road type "town” was estimated, this output is maintained for a travel distance of the vehicle of 500 M. If the road type "highway” was estimated, this type can be maintained for 1000 m.
- forming an overall estimate involves setting the overall estimate to the estimator using the state machine, if the estimator using the state machine does not output that no road type can be determined, forming an overall estimate further includes setting the overall estimate to the estimation using of the decision tree, when the estimation using the state machine outputs that no road type can be determined, which is particularly advantageous in the case of a machine-learning-induced decision tree and a human-based state As long as the road type can be determined using the state machine, and thus with rules based on human experience, preference is given to this estimate of road type.
- the machine learning-induced decision tree is used to provide a statement.
- Today's and future assistance systems can improve or optimize their function based on the type of road and are dependent on the ongoing provision of this estimate. The function of these systems is thus ensured.
- a cost-causing adaptation of the assistance system to vehicles with or without navigation system is thus avoided.
- the procedure Revert to typically existing sensors and resources or their evaluations, which does not require additional components. Which in turn saves costs.
- a reduction of the cable expenditure, the construction volume and the programming effort is also possible by the method according to the invention. Furthermore, a vehicle configuration becomes more flexible because it is not necessary to select a navigation system for driver assistance functions that require an estimation or determination of the road type. Both the decision tree and the state machine can be chosen such that the number of environment data needed for the estimation is minimized. In this way, the computational effort can be reduced. The reduction in the number of environmental data can also lead to savings in the power supply of the sensor systems, since unnecessary sensors and their electronic computing systems can be switched off. Usually, these sensor systems operate independently of each other, and the shutdown of a sensor system reduces power consumption accordingly.
- An assistance system may be an adaptive cruise control (ACC), a high beam assistant, a parking aid, a thermal management, a transmission management or an energy management.
- assigning a qualification value to the overall estimate is based on whether the overall estimate is based on the estimate using the state machine or the decision tree.
- the qualification value allows customers of the overall estimate (eg assistance systems) to assess the reliability of the overall estimate. For example, an overall estimate based on both the state machine estimate and the decision tree decision leading to the same result may be assigned a better (possibly higher) qualification score than the guess using the decision tree the state machine is not confirmed.
- An overall estimate based solely on the decision tree estimate may result in a lower (possibly lower) qualification score be assigned as if the overall estimate is at least also based on an estimate using the state machine.
- the method includes receiving a set of sensor-based environment data, each element of the set being assigned a time; For each element of the sentence: making a partial estimation of the road type using the decision tree based on the respective element of the sentence;
- the assignment of the qualification value is also based on the number of partial estimates which agree with the overall estimate. In particular, this may mean that each received element is based on (at least) one sensor measurement that was executed at a different time. In this way, a larger area of the environment of the vehicle is detected and used by dividing into samples for part estimation using the decision tree.
- the qualification value may be determined such that each partial estimate with the same result as the result of the state machine estimation increases the qualification value. However, partial estimates with a different result are not.
- the method includes receiving a set of sensor-based environment data, wherein each item of the set is assigned a time;
- estimating using the decision tree comprises: for each element of the sentence: making a partial estimate of the road type using the decision tree based on the particular element of the sentence; Estimate the road type based on the partial estimates made, in particular by means of a majority decision.
- a set of environment data includes at least two separate environment data.
- a set of environment data comprises at least two environment vectors.
- An element of the sentence is a single vector. Typically, each element is based on sensor data measured at a time.
- the estimate using the decision tree is thus based on multiple partial estimates using the decision tree, using environment data that is assigned to a different point in time.
- This can be special mean that each received element is based on (at least) one sensor measurement that was performed at a different time or at a different position of the vehicle. In this way, a larger area of the surroundings of the vehicle is recorded and taken into account for the estimation with the aid of the decision tree. The reliability of the estimation using the decision tree is thus increased.
- the method comprises determining a functional parameter of a sensor based on the overall estimate of the road type, in particular the strength of the power supply or the deactivation of the power supply.
- the function of the sensors can be adapted to the situation of the road type, which on the one hand can increase the performance of the sensors and at the same time save energy.
- the sensor can be placed in a lower power consumption mode.
- the range of ultrasonic sensors depends on the amount of energy / power supply provided. In cases where obstacles are to be detected at short distances (for example when parking in urban areas), they can therefore operate with a lower power supply than in cases where obstacles can be detected at greater distances (when detecting other road users on the road) Highway for example).
- an electronic data processing unit and environment data reception unit is configured to execute one of the above-presented methods.
- This device may be a microprocessor, a general-purpose computer or dedicated circuits. The device can be set up by program code for executing the method.
- the receiving unit for environment data may be a standard interface such as USB, CAN, Ethernet, Wi-Fi or Firewire.
- Fig. 1 shows schematically the structure of a system according to an embodiment example.
- Fig. 2 shows schematically the structure of a device for estimating the road type according to an embodiment.
- Fig. 3 shows schematically the generation of a decision tree and the decision tree according to an embodiment.
- 4 schematically shows a state machine according to an exemplary embodiment.
- FIG. 1 schematically shows the structure of a system according to an embodiment.
- Sensors of a vehicle such as a side camera, a front-facing camera, ultrasonic sensors, lidar sensors, and chassis capacitive sensors provide environmental data to an apparatus 1 for estimating the road type.
- This environment data may be: street lamps detected by the side camera, by the front-facing camera detected oncoming traffic on the road, by the front-facing camera detected traffic signs, a speed limit detected by the front-facing camera, obstacles detected by the ultrasonic sensors on the vehicle Lane, a detected by the ultrasonic sensors distance to a vehicle ahead, a detected by the Lidarsensoren brake threshold, a detected by the capacitive sensors height of a chassis.
- the environment data estimating apparatus 1 estimates the road type based on the surrounding data, for which an electronic computing unit included in the apparatus 1 is used.
- the estimated road type is provided to various driver assistance functions. For example, an Automatic Cruise Control (ACC), a high-beam assistant, a Park Maneuver Assistance (PMA), a Park Distance Control (PDC) and a prepared view of the vehicle from above, based on several cameras of the vehicle (top view).
- ACC Automatic Cruise Control
- PMA Park Maneuver Assistance
- PDC Park Distance Control
- the estimated road ß type the sensors or sensor systems provided that can be deactivated according to the estimated type to save energy.
- FIG. 2 shows schematically the structure of a device 1 for estimating the road type according to an exemplary embodiment.
- the device receives environment data and passes it on to both means 2 for estimating the road type by means of a decision tree, and to means 3 for estimating the road type by means of a state machine.
- Both means 2 and 3 provide an estimate of the road type (if appropriate, the means 3 provide the output "no estimate of road type possible") to total estimate estimation means 4.
- These means 4 form an overall estimate based on the outputs of means 2 and 3. It is envisaged that as long as the estimation using the state machine outputs a road type and does not output the output "no estimation of the road type possible", this estimation of the state machine forms the total estimate.
- the estimate is provided using the decision tree as an overall estimate.
- the total estimate is output from the device 1.
- the means 2, 3 and 4 can be implemented both in hardware and in software. The means 2 and 3 do not have to make the estimate at the same time but can also make them one after the other.
- Fig. 3 shows schematically the generation of a decision tree and the decision tree.
- the decision tree induction algorithm obtains as training data the vectors of environmental data recorded during a trip and the road type determined simultaneously with each vector, which is determined via a navigation system having a digital map storing the road type. Algorithms for creating or inducing the decision tree are known in the art. If the road types stored in the digital map are classified finer than the classification that the decision tree is to deliver, road types stored in the map must be grouped into a road type for the decision tree. In FIG. 3, the letter “u” designates the estimate “urban”, the letter the estimate "highway” (rural) and the letter “m” the estimate "motorway”.
- the environment data vector may include a detected speed limit, a recognized lane width, a recognized overtaking ban, the number of signs left / right / total, left or right recognized objects.
- the generated decision tree is integrated into the source code of an application or a program, and a compiler is translated into machine language / object code.
- the input environment data (the input vector) is propagated top-down through the generated "if-then-else" construct, which contains a (threshold) query in each node, based on a value of the input vector
- the sheets each contain the results of such a query path, which at the same time corresponds to one of the classes to be classified, ie the road type estimate
- the thresholds at which the input data are compared occupy different amounts of space according to their resolution
- the first way to optimize memory space is to map certain continuous (analogue ) Values to the discrete interval 0-255 if the loss of accuracy is acceptable for the application.
- defining a custom language and implementing a corresponding parser provides another way to reduce storage space. It defines a reduced and adjusted alphabet of operators and operands designed specifically for the decision tree. An inquiry node can therefore be reduced to a few bytes in most cases.
- the memory consumption of non-randomly writable memory (ROM) of a decision tree could thus be reduced to less than 8 KB with a general overhead of less than 7 KB.
- the need for RAM was negligible. It is also possible to have your own decision tree for a specific country or region. It is also possible to provide a separate decision tree for each assistance function.
- 4 schematically shows a state machine according to an exemplary embodiment.
- Rule 1 defines that for received environment data indicating that a sign was detected indicating a quiet traffic area (game road), it is transitioned to the "in-town” state, in which case the state machine provides an estimate that depends on the state indicating that no estimation is possible, the estimation of the state machine ("in-town") is output by the road-type estimation apparatus 1. This estimate is issued for 500m because the detected road type is "in town".
- Rule 2 defines that for received environment data indicating that a sign was detected indicating a location exit, it is transitioned to the state "highway.”
- the state machine provides an estimate that indicates the state that indicates that no estimation is possible deviates, the estimation of the state machine ("highway") is output by the device 1 for estimating the road type. This estimate is output for 1000m since the detected road type is "highway".
- Rule 3 defines that in the case of received environment data indicating that the vehicle has been traveling faster than 120 km / h for 5 minutes, it is transitioning to the state "Highway.”
- the state machine provides an estimate that depends on the state indicating that no estimation is possible, the estimation of the state machine ("highway") is output by the road-type estimation apparatus 1. This estimate is issued for 2000m since the recognized road type is "highway".
- the state machine could be realized with a memory consumption of non-randomly writable memory (ROM) of less than 3 KB with a general overhead of less than 7 KB.
- ROM non-randomly writable memory
- the need for RAM was negligible. It is also possible to have your own state machine for a specific country or country certain region. It is also possible to provide a separate state machine for each assistance function.
- the output of the road-type estimator 1 may be assigned a qualification value.
- This qualification value is designed based on the estimates using the decision tree or the state machine. At the same time, for this qualification value, the estimate using the decision tree is performed three times (or more times), each time with other environment data vectors representing sensor measurements at different times or positions of the vehicle. The multiple estimates using the decision tree can be performed while only one estimate is being performed using the state machine.
- the qualification value is then formed as follows, where a higher qualification value indicates a better overall estimate: value 5 if the estimation using the state machine is confirmed by at least two estimates using the decision tree; Value 4 if the estimation using the state machine is confirmed by an estimate using the decision tree; Value 3 if the estimation using the state machine is not confirmed by any estimate using the decision tree; Value 2 if the state machine outputs that no estimate is possible and three estimates use the decision tree to output the same estimate (which is also output as the total estimate); Value 1 if the state machine returns that no estimate is possible and two estimates use the decision tree to output the same estimate (which is also output as the total estimate).
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Abstract
Description
Claims
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE102012218362.0A DE102012218362A1 (en) | 2012-10-09 | 2012-10-09 | Estimation of the road type using sensor-based environmental data |
PCT/EP2013/070314 WO2014056746A1 (en) | 2012-10-09 | 2013-09-30 | Estimating a street type using sensor-based surroundings data |
Publications (2)
Publication Number | Publication Date |
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EP2907120A1 true EP2907120A1 (en) | 2015-08-19 |
EP2907120B1 EP2907120B1 (en) | 2021-03-17 |
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ID=49293638
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
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EP13771129.7A Active EP2907120B1 (en) | 2012-10-09 | 2013-09-30 | Estimating a street type using sensor-based surroundings data |
Country Status (4)
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US (1) | US10072936B2 (en) |
EP (1) | EP2907120B1 (en) |
DE (1) | DE102012218362A1 (en) |
WO (1) | WO2014056746A1 (en) |
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US8543254B1 (en) * | 2012-03-28 | 2013-09-24 | Gentex Corporation | Vehicular imaging system and method for determining roadway width |
US8949016B1 (en) * | 2012-09-28 | 2015-02-03 | Google Inc. | Systems and methods for determining whether a driving environment has changed |
-
2012
- 2012-10-09 DE DE102012218362.0A patent/DE102012218362A1/en not_active Ceased
-
2013
- 2013-09-30 EP EP13771129.7A patent/EP2907120B1/en active Active
- 2013-09-30 WO PCT/EP2013/070314 patent/WO2014056746A1/en active Application Filing
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2015
- 2015-04-08 US US14/681,589 patent/US10072936B2/en active Active
Non-Patent Citations (1)
Title |
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See references of WO2014056746A1 * |
Also Published As
Publication number | Publication date |
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WO2014056746A1 (en) | 2014-04-17 |
US20150211867A1 (en) | 2015-07-30 |
DE102012218362A1 (en) | 2014-04-24 |
EP2907120B1 (en) | 2021-03-17 |
US10072936B2 (en) | 2018-09-11 |
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